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Recently, reinforcement learning (RL) algorithms have demonstrated remarkable success in learning complicated behaviors from minimally processed input.
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M. Zhang, X. Geng, J. Bruce, K. Caluwaerts, M. Vespignani, V. SunSpiral, P. Abbeel, and S. Levine, “Deep reinforcement learning for tensegrity robot locomotion,” in 2017 IEEE International Conference on Robotics and Automation (ICRA) , May 2017, pp. 634–641
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G. Papamakarios, T. Pavlakou, and I. Murray, “Masked autoregressive flow for density estimation,” in Advances in Neural Information Processing Systems , 2017, pp. 2338–2347
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2018
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2019
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